Pulmonary surfactant is essential for gas exchange and required for adaptation to air breathing at birth and thereafter. Defects in the surfactant system are known to be associated with common pulmonary disorders including neonatal respiratory distress syndrome, a major cause of mortality and morbidity in preterm infants. Little is known regarding the genetic regulation of the surfactant system, in particular the genes and associated transcriptional networks serve to induce the critical physiological program at birth. Our long-term goal is to prevent and control common pulmonary disorders associated with surfactant deficiency including neonatal respiratory distress syndrome and acute respiratory distress syndrome in children and adults. The objective of this application is to integrate computational and experimental approaches to identify critical regulators of surfactant lipid homeostasis during lung maturation. This application seek to test the central hypothesis that SREBP (sterol regulatory element binding protein) signaling is a key component in the transcriptional network sensing and regulating genes and processes critical for surfactant homeostasis during lung maturation. We plan to accomplish our objective by pursuing the following three Specific Aims: 1). Identify a transcriptional network controlling perinatal surfactant lipid homeostasis; 2). Determine the mechanisms by which SREBP influences perinatal lung lipid homeostasis; and 3) Determine the critical perinatal regulatory components in the SREBP network. Under Aim 1, we will develop and refine a transcriptional network controlling surfactant lipid homeostasis during lung maturation. In Aim 2, we will determine and evaluate the direct vs. compensatory roles of SREBP in perinatal lung in vivo using SREBP deletion/activation transgenic mouse models. In Aim 3, we will validate biological relevant upstream and downstream genes in SREBP centered signaling pathway in vivo and in vitro. The experimental results will be used to further refine the transcriptional network. The project is highly innovative, new algorithms will be developed for data integration and network model refinement; newly developed transgenic mouse models and highly refined lung mRNA microarray data from mouse strains with distinct lung maturation programs will be used, from which, a dynamic transcriptional network controlling perinatal lung surfactant lipid homeostasis will be constructed for the first time in surfactant biology. The proposed research is significant, a comprehensive understanding of the transcriptional program controlling perinatal lung surfactant level will provide scientific basis for development of diagnostic reagents, disease markers and therapeutic interventions aimed at preventing or ameliorating pulmonary mortality and morbidity associated with surfactant deficiency. The synergistic integration of computational and experimental approaches developed in this application will be highly relevant to studies related to many other research areas. The microarray datasets and network models we developed will benefit the scientific community at large.